Collaborative Research: NeTS: Medium: An Integrated Multi-Time Scale Approach to High-Performance, Intelligent, and Secure O-RAN based NextG

合作研究：NeTS：Medium：基于 NextG 的高性能、智能和安全 O-RAN 的集成多时间尺度方法

• 批准号: 2312448
• 负责人: Huacheng Zeng
• 金额: $ 30万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312448&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; Medium; Integrated

Recent movement to open up radio access network (RAN) interfaces, led by the O-RAN Alliance, has introduced a new paradigm for future wireless networks. With its key features of openness and intelligence, O-RAN enables a ``mix-and-match” approach to RAN development and deployment, allowing telecom carriers to select the best hardware and software from different vendors. Such openness also catalyzes the integration of machine learning (ML) based intelligence into the RAN and promises further performance improvement. This project aims to address several major challenges in O-RAN, with the objective of enhancing its performance, intelligence, and trustworthiness. Through innovation in wireless algorithm and protocol design, ML, and network security, this project expedites the evolution of O-RAN ecosystem. The outcomes of this project provide valuable insights to the wireless industry and academic research community regarding new potentials, challenges, and innovative solutions surrounding O-RAN technologies. Moreover, the project promotes the participation of women and students with diverse backgrounds in wireless communications and computer science research while enhancing pedagogical activities through new course materials.This project aims to enhance the performance, intelligence, and trustworthiness of O-RAN by tackling several fundamental challenges across its control loops of three different time scales. The project consists of three interconnected research thrusts. The first thrust focuses on real-time multi-user multi-input and multi-output (MU-MIMO) beamforming in O-RAN’s distributed unit (O-DU). It develops a data-driven approach for beamforming that accounts for channel uncertainty. The second thrust focuses on the design of ML algorithms for MU-MIMO control within the near-RT RAN Intelligent Controller (RIC). It establishes an optimization-based framework to generate high-quality labeled datasets for training ML models. The third thrust aims to advance knowledge of the vulnerabilities of ML models in the non-RT RIC of O-RAN and develop safeguard solutions against data manipulation attacks. The three research thrusts are tightly integrated vertically within the O-RAN architecture and their outcome lays the foundation for designing a comprehensive solution for O-RAN.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由O-Ran Alliance领导的最近开放无线电访问网络（RAN）接口的运动引入了新的无线网络范式。 O-Ran凭借其开放性和智能的关键特征，可以采用开发和部署的``混合匹配''方法，从而使电信运营商可以从不同供应商中选择最佳的硬件和软件。这种开放性还催化基于机器学习（ML）智能的整合到跑步中，并承诺进一步提高性能。该项目旨在应对O-Ran的几个主要挑战，目的是提高其表现，智慧和可信度。通过无线算法和协议设计，ML和网络安全性的创新，该项目加快了O-Ran生态系统的发展。该项目的结果为无线行业和学术研究界提供了有关O-RAN技术的新潜力，挑战和创新解决方案的宝贵见解。此外，该项目促进了妇女和学生在无线通信和计算机科学研究中的参与，同时通过新课程材料增强教学活动。该项目旨在通过在其三个不同时间尺度的控制循环中应对几个基本挑战，从而提高O-Ran的性能，智力和可信度。该项目由三个相互联系的研究推力组成。第一个推力着重于O-Ran分布式单元（O-DU）中的实时多用户多输入和多输出（MU-MIMO）。它开发了一种数据驱动的方法，用于计算通道不确定性。第二个推力着重于在近距离RAN智能控制器（RIC）内用于MU-MIMO控制的ML算法的设计。它建立了一个基于优化的框架，以生成用于培训ML模型的高质量标签数据集。第三个力量旨在提高O-Ran非RT RIC中ML模型的脆弱性的了解，并开发针对数据操纵攻击的保障解决方案。这三个研究的推力是在O-RAN建筑中垂直整合的，其结果为O-Ran设计全面的解决方案奠定了基础。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得的支持。